1. Gastos (cálculos antiguos)

Gastos_casa %>% 
  dplyr::select(-Tiempo,-link) %>%
  dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
  knitr::kable(.,format = "html", format.args = list(decimal.mark = ",", big.mark = "."),
                   caption="Tabla 1. Gastos Casa (últimos 30 registros)", align =rep('c', 3)) %>%
    kableExtra::kable_styling(bootstrap_options = c("striped", "hover"),font_size = 8) %>%
    kableExtra::scroll_box(width = "100%", height = "300px")
Tabla 1. Gastos Casa (últimos 30 registros)
fecha gasto monto gastador obs
24/6/2022 Comida 40.400 Andrés Bar la Providencia
27/6/2022 Agua 12.502 Andrés PAC AGUAS ANDIN 000000005687837
29/6/2022 Netflix 8.320 Tami NA
29/6/2022 Comida 68.213 Tami NA
30/6/2022 Comida 15.310 Tami NA
30/6/2022 Electricidad 67.655 Andrés NA
2/7/2022 Diosi 35.990 Andrés NA
3/7/2022 Gas 19.600 Andrés NA
3/7/2022 Parafina 44.029 Tami NA
11/7/2022 Diosi 15.930 Tami NA
11/7/2022 Comida 60.660 Tami NA
14/7/2022 Enceres 18.990 Andrés NA
15/7/2022 Ropa 18.990 Andrés NA
15/7/2022 Ropa 18.990 Andrés NA
15/7/2022 Comida 15.000 Andrés NA
19/7/2022 Parafina 22.521 Tami NA
20/7/2022 VTR 21.990 Andrés NA
21/7/2022 Comida 24.660 Andrés NA
23/7/2022 Enceres 14.315 Andrés NA
23/7/2022 Comida 22.263 Andrés NA
20/7/2022 Comida 41.830 Andrés NA
25/7/2022 Comida 61.470 Tami NA
25/7/2022 Comida 16.100 Tami NA
25/7/2022 Cortina baño 29.120 Tami NA
28/7/2022 Electricidad 78.798 Andrés NA
29/7/2022 Netflix 8.320 Tami NA
30/7/2022 Comida 36.170 Tami NA
31/7/2022 Parafina 22.060 Tami NA
31/3/2019 Comida 9.000 Andrés NA
8/9/2019 Comida 24.588 Andrés Super Lider

#para ver las diferencias depués de la diosi
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::group_by(gastador, fecha,.drop = F) %>% 
    dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>% 
    dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
    #dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de  diosi. Junio 24, 2019 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
    assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv) 

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")

par(mfrow=c(1,2)) 
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))

library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
  dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
  dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
  dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
  dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
  dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
  dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
#  dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
  #dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>% 
  dplyr::group_by(gastador_nombre, fecha_simp) %>%
  dplyr::summarise(monto_total=sum(monto)) %>%
  dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
  ggplot(aes(hover_css = "fill:none;")) +#, ) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
                       ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
 # guides(color = F)+
  sjPlot::theme_sjplot2() +
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
     theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

#  x <- girafe(ggobj = gg)
#  x <- girafe_options(x = x,
#                      opts_hover(css = "stroke:red;fill:orange") )
#  if( interactive() ) print(x)

#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"

#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
    dplyr::group_by(month)%>%
    dplyr::summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = month, y = gasto_total)) +
      geom_point()+
      geom_line(size=1) +
      sjPlot::theme_sjplot2() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Mes") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot)  
plot2<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = day, y = gasto_total)) +
      geom_line(size=1) +
      sjPlot::theme_sjplot2() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Día") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot2)  
tsData <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
  data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
  tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
    theme_bw()+ labs(x="Weeks")

tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
   #it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
   #ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan. 
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

itsa_metro_region_quar2<-
        its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
                                 interrupt_var = "covid", 
                                 alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 

print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
## 
## $aov.result
## Anova Table (Type II tests)
## 
## Response: depvar
##                   Sum Sq  Df   F value Pr(>F)    
## interrupt_var 4.2487e+08   2    4.7379 0.0092 ** 
## lag_depvar    7.3897e+10   1 1648.1010 <2e-16 ***
## Residuals     2.1163e+10 472                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $tukey.result
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
## 
## $`x$interrupt_var`
##          diff       lwr      upr     p adj
## 1-0  7228.838   895.069 13562.61 0.0205774
## 2-0 27953.942 22094.712 33813.17 0.0000000
## 2-1 20725.104 17157.287 24292.92 0.0000000
## 
## 
## $data
##        depvar interrupt_var lag_depvar
## 2    19269.29             0   16010.00
## 3    24139.00             0   19269.29
## 4    23816.14             0   24139.00
## 5    26510.14             0   23816.14
## 6    23456.71             0   26510.14
## 7    24276.71             0   23456.71
## 8    18818.71             0   24276.71
## 9    18517.14             0   18818.71
## 10   15475.29             0   18517.14
## 11   16365.29             0   15475.29
## 12   12621.29             0   16365.29
## 13   12679.86             0   12621.29
## 14   13440.71             0   12679.86
## 15   15382.86             0   13440.71
## 16   13459.71             0   15382.86
## 17   14644.14             0   13459.71
## 18   13927.00             0   14644.14
## 19   22034.57             0   13927.00
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## 21   20390.57             0   20986.00
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## 225  85013.86             2   91884.00
## 226  84535.29             2   85013.86
## 227  80700.43             2   84535.29
## 228  79740.57             2   80700.43
## 229  85163.14             2   79740.57
## 230  86724.86             2   85163.14
## 231  80355.00             2   86724.86
## 232  74875.14             2   80355.00
## 233  81347.00             2   74875.14
## 234  66062.43             2   81347.00
## 235  56946.43             2   66062.43
## 236  47732.14             2   56946.43
## 237  38129.71             2   47732.14
## 238  42928.29             2   38129.71
## 239  45392.57             2   42928.29
## 240  37895.43             2   45392.57
## 241  30660.29             2   37895.43
## 242  42430.86             2   30660.29
## 243  35845.14             2   42430.86
## 244  40350.43             2   35845.14
## 245  31494.71             2   40350.43
## 246  30013.29             2   31494.71
## 247  34197.57             2   30013.29
## 248  37430.14             2   34197.57
## 249  26932.43             2   37430.14
## 250  33729.86             2   26932.43
## 251  38081.43             2   33729.86
## 252  44028.00             2   38081.43
## 253  47139.71             2   44028.00
## 254  46558.86             2   47139.71
## 255  58350.57             2   46558.86
## 256  78380.00             2   58350.57
## 257  78168.29             2   78380.00
## 258  70510.86             2   78168.29
## 259  72207.14             2   70510.86
## 260  67881.00             2   72207.14
## 261  69536.43             2   67881.00
## 262  62390.71             2   69536.43
## 263  50113.14             2   62390.71
## 264  45565.57             2   50113.14
## 265  45805.29             2   45565.57
## 266  41348.57             2   45805.29
## 267  51426.86             2   41348.57
## 268  47160.57             2   51426.86
## 269  51907.43             2   47160.57
## 270  49751.43             2   51907.43
## 271  54407.43             2   49751.43
## 272  54746.29             2   54407.43
## 273  61634.57             2   54746.29
## 274  58926.43             2   61634.57
## 275  69999.29             2   58926.43
## 276  63044.86             2   69999.29
## 277  63285.29             2   63044.86
## 278  61395.43             2   63285.29
## 279  67969.43             2   61395.43
## 280  60792.57             2   67969.43
## 281  56859.14             2   60792.57
## 282  44899.43             2   56859.14
## 283  43064.14             2   44899.43
## 284  62790.29             2   43064.14
## 285  69120.71             2   62790.29
## 286  69589.43             2   69120.71
## 287  66633.29             2   69589.43
## 288  65588.57             2   66633.29
## 289  70168.57             2   65588.57
## 290  74644.71             2   70168.57
## 291  52891.00             2   74644.71
## 292  41560.57             2   52891.00
## 293  34704.86             2   41560.57
## 294  46520.00             2   34704.86
## 295  50231.00             2   46520.00
## 296  49216.71             2   50231.00
## 297  76914.86             2   49216.71
## 298  83720.71             2   76914.86
## 299  84485.00             2   83720.71
## 300  89765.00             2   84485.00
## 301  87702.86             2   89765.00
## 302  82013.86             2   87702.86
## 303  85982.43             2   82013.86
## 304  57248.43             2   85982.43
## 305  52968.43             2   57248.43
## 306  52601.86             2   52968.43
## 307  45493.29             2   52601.86
## 308  42298.86             2   45493.29
## 309  46423.71             2   42298.86
## 310  37898.00             2   46423.71
## 311  36435.14             2   37898.00
## 312  30209.57             2   36435.14
## 313  34541.86             2   30209.57
## 314  33604.71             2   34541.86
## 315  37990.71             2   33604.71
## 316  35683.43             2   37990.71
## 317  65201.86             2   35683.43
## 318  62730.57             2   65201.86
## 319  64589.14             2   62730.57
## 320  73744.86             2   64589.14
## 321  76477.71             2   73744.86
## 322 105647.43             2   76477.71
## 323 103790.29             2  105647.43
## 324  76122.29             2  103790.29
## 325  74746.14             2   76122.29
## 326  72865.71             2   74746.14
## 327  63652.57             2   72865.71
## 328  60358.29             2   63652.57
## 329  25957.14             2   60358.29
## 330  30178.43             2   25957.14
## 331  30681.57             2   30178.43
## 332  33337.29             2   30681.57
## 333  32582.71             2   33337.29
## 334  39184.43             2   32582.71
## 335  40415.71             2   39184.43
## 336  34975.43             2   40415.71
## 337  34076.14             2   34975.43
## 338  34221.14             2   34076.14
## 339  28862.57             2   34221.14
## 340  35729.86             2   28862.57
## 341  36489.29             2   35729.86
## 342  36785.14             2   36489.29
## 343  37787.71             2   36785.14
## 344  39832.14             2   37787.71
## 345  41917.86             2   39832.14
## 346  41633.57             2   41917.86
## 347  33557.00             2   41633.57
## 348  22759.57             2   33557.00
## 349  28877.86             2   22759.57
## 350  27574.00             2   28877.86
## 351  27104.71             2   27574.00
## 352  24376.14             2   27104.71
## 353  29732.29             2   24376.14
## 354  34030.00             2   29732.29
## 355  39139.71             2   34030.00
## 356  37066.57             2   39139.71
## 357  38509.29             2   37066.57
## 358  40957.29             2   38509.29
## 359  49423.00             2   40957.29
## 360  50053.29             2   49423.00
## 361  50284.14             2   50053.29
## 362  53103.86             2   50284.14
## 363  50223.00             2   53103.86
## 364  49587.14             2   50223.00
## 365  41167.71             2   49587.14
## 366  37958.71             2   41167.71
## 367  33582.29             2   37958.71
## 368  31039.43             2   33582.29
## 369  26526.57             2   31039.43
## 370  34869.43             2   26526.57
## 371  37487.43             2   34869.43
## 372  46514.43             2   37487.43
## 373  39613.43             2   46514.43
## 374  38980.57             2   39613.43
## 375  37306.14             2   38980.57
## 376  36771.29             2   37306.14
## 377  26317.00             2   36771.29
## 378  31580.71             2   26317.00
## 379  23626.57             2   31580.71
## 380  33035.71             2   23626.57
## 381  44864.57             2   33035.71
## 382  48946.14             2   44864.57
## 383  46969.57             2   48946.14
## 384  49249.57             2   46969.57
## 385  56370.14             2   49249.57
## 386  67228.71             2   56370.14
## 387  59457.29             2   67228.71
## 388  53124.71             2   59457.29
## 389  52814.14             2   53124.71
## 390  61262.00             2   52814.14
## 391  61861.14             2   61262.00
## 392  71784.71             2   61861.14
## 393  59313.29             2   71784.71
## 394  61107.00             2   59313.29
## 395  60603.43             2   61107.00
## 396  60012.57             2   60603.43
## 397  58280.43             2   60012.57
## 398  56862.71             2   58280.43
## 399  41704.43             2   56862.71
## 400  51533.00             2   41704.43
## 401  50388.71             2   51533.00
## 402  49205.29             2   50388.71
## 403  56533.29             2   49205.29
## 404  47996.14             2   56533.29
## 405  47207.57             2   47996.14
## 406  45292.00             2   47207.57
## 407  40343.43             2   45292.00
## 408  39004.86             2   40343.43
## 409  36788.43             2   39004.86
## 410  30027.57             2   36788.43
## 411  39040.14             2   30027.57
## 412  42390.14             2   39040.14
## 413  36291.14             2   42390.14
## 414  30668.29             2   36291.14
## 415  47693.00             2   30668.29
## 416  52094.43             2   47693.00
## 417  56592.57             2   52094.43
## 418  47971.43             2   56592.57
## 419  43762.43             2   47971.43
## 420  42246.71             2   43762.43
## 421  46352.43             2   42246.71
## 422  33094.86             2   46352.43
## 423  32784.86             2   33094.86
## 424  26212.43             2   32784.86
## 425  32611.57             2   26212.43
## 426  42144.86             2   32611.57
## 427  50034.86             2   42144.86
## 428  46332.00             2   50034.86
## 429  42976.29             2   46332.00
## 430  39456.29             2   42976.29
## 431  39328.29             2   39456.29
## 432  35296.14             2   39328.29
## 433  30875.43             2   35296.14
## 434  27709.00             2   30875.43
## 435  29513.29             2   27709.00
## 436  31630.43             2   29513.29
## 437  29346.14             2   31630.43
## 438  34916.86             2   29346.14
## 439  42020.86             2   34916.86
## 440  38303.00             2   42020.86
## 441  37966.43             2   38303.00
## 442  41408.14             2   37966.43
## 443  38988.14             2   41408.14
## 444  43555.29             2   38988.14
## 445  38114.00             2   43555.29
## 446  27847.86             2   38114.00
## 447  26517.00             2   27847.86
## 448  39518.29             2   26517.00
## 449  39153.71             2   39518.29
## 450  45623.14             2   39153.71
## 451  40627.43             2   45623.14
## 452  41027.71             2   40627.43
## 453  42882.86             2   41027.71
## 454  47139.43             2   42882.86
## 455  35547.57             2   47139.43
## 456  41099.00             2   35547.57
## 457  35859.57             2   41099.00
## 458  44524.57             2   35859.57
## 459  48554.29             2   44524.57
## 460  51554.29             2   48554.29
## 461  47810.29             2   51554.29
## 462  50490.00             2   47810.29
## 463  50720.71             2   50490.00
## 464  52720.71             2   50720.71
## 465  52145.57             2   52720.71
## 466  55515.57             2   52145.57
## 467  52457.00             2   55515.57
## 468  58239.57             2   52457.00
## 469  50523.57             2   58239.57
## 470  47788.57             2   50523.57
## 471  46170.00             2   47788.57
## 472  42305.57             2   46170.00
## 473  46605.57             2   42305.57
## 474  55149.57             2   46605.57
## 475  48769.57             2   55149.57
## 476  50719.43             2   48769.57
## 477  44753.71             2   50719.43
## 
## $alpha
## [1] 0.05
## 
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
## 
## $group.means
##   interrupt_var count     mean      s.d.
## 1             0    37 22066.04  6308.636
## 2             1   120 29463.10  9187.258
## 3             2   320 50188.20 16190.539
## 
## $dependent
##   [1]  19269.29  24139.00  23816.14  26510.14  23456.71  24276.71  18818.71
##   [8]  18517.14  15475.29  16365.29  12621.29  12679.86  13440.71  15382.86
##  [15]  13459.71  14644.14  13927.00  22034.57  20986.00  20390.57  22554.14
##  [22]  21782.57  22529.57  24642.71  17692.29  19668.29  28640.00  28706.00
##  [29]  28331.57  25617.86  27223.29  31622.57  32021.43  33634.57  30784.86
##  [36]  34770.57  38443.00  35073.00  31422.29  30103.29  19319.29  27926.29
##  [43]  30715.43  31962.29  39790.14  39211.57  44548.57  49398.00  41039.00
##  [50]  34821.29  29123.57  21275.71  28476.14  24561.86  20323.57  25370.00
##  [57]  26811.86  27151.86  27623.29  22896.57  41889.29  44000.14  38558.00
##  [64]  43373.86  49001.00  61213.29  58939.57  42046.86  39191.71  42646.43
##  [71]  36121.57  30915.57  20273.43  23938.29  19274.29  21662.29  15819.00
##  [78]  18126.14  17240.71  16127.71  13917.14  15379.86  19510.14  24567.29
##  [85]  25700.43  25729.00  26435.00  31157.14  29818.43  30962.43  28746.71
##  [92]  27830.71  28252.14  28717.57  21365.43  24816.86  16838.57  15529.14
##  [99]  13286.29  13629.43  14404.86  19524.86  18475.71  22495.00  22254.57
## [106]  24173.29  27466.43  24602.43  20531.14  20846.43  23875.71  36312.71
## [113]  34244.00  36347.43  39779.71  42018.71  39372.57  33444.00  29255.86
## [120]  31640.14  29671.14  31023.71  39723.43  39314.14  38239.86  34649.43
## [127]  36688.43  42867.57  42226.86  32155.14  33603.00  37254.43  33145.57
## [134]  31299.43  30252.00  26310.71  27929.86  27666.14  25017.57  27335.00
## [141]  25760.71  18436.86  21906.00  19418.14  22826.14  23444.29  25264.86
## [148]  25473.29  27366.86  28855.86  32326.86  27141.43  26297.71  23499.14
## [155]  30246.29  39931.86  38020.43  35004.00  40750.86  42363.29  46273.57
## [162]  41083.29  35711.29  41921.71  60583.29  63115.57  61300.14  57666.43
## [169]  55834.00  58927.71  57810.57  48987.14  52219.29  56503.57  56545.00
## [176]  64705.57  53833.29  50114.00  39592.43  29907.29  33923.29  45489.00
## [183]  44866.29  51680.57  58257.00  70600.57  76648.00  69430.14  69651.57
## [190]  77745.14  72795.86  67670.71  55357.86  48524.00  50154.43  45111.57
## [197]  36147.00  43501.57  41472.43  41058.00  41605.57  49382.86  59558.57
## [204]  59134.57  61109.00  63004.43  67344.29  78180.86  69117.86  55597.57
## [211]  49426.14  39119.43  35636.86  39201.14  27777.00  47207.00  55587.29
## [218]  56619.71  82679.86  91259.57  93552.71 102242.71  91884.00  85013.86
## [225]  84535.29  80700.43  79740.57  85163.14  86724.86  80355.00  74875.14
## [232]  81347.00  66062.43  56946.43  47732.14  38129.71  42928.29  45392.57
## [239]  37895.43  30660.29  42430.86  35845.14  40350.43  31494.71  30013.29
## [246]  34197.57  37430.14  26932.43  33729.86  38081.43  44028.00  47139.71
## [253]  46558.86  58350.57  78380.00  78168.29  70510.86  72207.14  67881.00
## [260]  69536.43  62390.71  50113.14  45565.57  45805.29  41348.57  51426.86
## [267]  47160.57  51907.43  49751.43  54407.43  54746.29  61634.57  58926.43
## [274]  69999.29  63044.86  63285.29  61395.43  67969.43  60792.57  56859.14
## [281]  44899.43  43064.14  62790.29  69120.71  69589.43  66633.29  65588.57
## [288]  70168.57  74644.71  52891.00  41560.57  34704.86  46520.00  50231.00
## [295]  49216.71  76914.86  83720.71  84485.00  89765.00  87702.86  82013.86
## [302]  85982.43  57248.43  52968.43  52601.86  45493.29  42298.86  46423.71
## [309]  37898.00  36435.14  30209.57  34541.86  33604.71  37990.71  35683.43
## [316]  65201.86  62730.57  64589.14  73744.86  76477.71 105647.43 103790.29
## [323]  76122.29  74746.14  72865.71  63652.57  60358.29  25957.14  30178.43
## [330]  30681.57  33337.29  32582.71  39184.43  40415.71  34975.43  34076.14
## [337]  34221.14  28862.57  35729.86  36489.29  36785.14  37787.71  39832.14
## [344]  41917.86  41633.57  33557.00  22759.57  28877.86  27574.00  27104.71
## [351]  24376.14  29732.29  34030.00  39139.71  37066.57  38509.29  40957.29
## [358]  49423.00  50053.29  50284.14  53103.86  50223.00  49587.14  41167.71
## [365]  37958.71  33582.29  31039.43  26526.57  34869.43  37487.43  46514.43
## [372]  39613.43  38980.57  37306.14  36771.29  26317.00  31580.71  23626.57
## [379]  33035.71  44864.57  48946.14  46969.57  49249.57  56370.14  67228.71
## [386]  59457.29  53124.71  52814.14  61262.00  61861.14  71784.71  59313.29
## [393]  61107.00  60603.43  60012.57  58280.43  56862.71  41704.43  51533.00
## [400]  50388.71  49205.29  56533.29  47996.14  47207.57  45292.00  40343.43
## [407]  39004.86  36788.43  30027.57  39040.14  42390.14  36291.14  30668.29
## [414]  47693.00  52094.43  56592.57  47971.43  43762.43  42246.71  46352.43
## [421]  33094.86  32784.86  26212.43  32611.57  42144.86  50034.86  46332.00
## [428]  42976.29  39456.29  39328.29  35296.14  30875.43  27709.00  29513.29
## [435]  31630.43  29346.14  34916.86  42020.86  38303.00  37966.43  41408.14
## [442]  38988.14  43555.29  38114.00  27847.86  26517.00  39518.29  39153.71
## [449]  45623.14  40627.43  41027.71  42882.86  47139.43  35547.57  41099.00
## [456]  35859.57  44524.57  48554.29  51554.29  47810.29  50490.00  50720.71
## [463]  52720.71  52145.57  55515.57  52457.00  58239.57  50523.57  47788.57
## [470]  46170.00  42305.57  46605.57  55149.57  48769.57  50719.43  44753.71
## 
## $interrupt_var
##   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
##  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
## 
## $residuals
##             2             3             4             5             6 
##   2066.949065   4060.967382   -558.480784   2420.378865  -3009.989201 
##             7             8             9            10            11 
##    504.077230  -5677.415860  -1163.346693  -3939.124757   -365.267897 
##            12            13            14            15            16 
##  -4894.522593  -1532.587590   -823.408525    447.423496  -3189.288798 
##            17            18            19            20            21 
##   -308.054654  -2070.229082   6670.083691  -1531.868115  -1202.133048 
##            22            23            24            25            26 
##   1486.790158  -1193.719051    234.045092   1688.103390  -7126.769396 
##            27            28            29            30            31 
##    981.653833   8209.926232    360.105139    -72.555803  -2455.908522 
##            32            33            34            35            36 
##   1543.853629   4526.655832   1043.985149   2305.212900  -1967.791277 
##            37            38            39            40            41 
##   4532.250564   4259.123340  -2351.092144  -3028.426290  -1126.369508 
##            42            43            44            45            46 
## -10746.604402   7375.212047   2570.324379   1356.296662   8084.040966 
##            47            48            49            50            51 
##    598.883493   6446.362067   6586.909390  -6050.783372  -4893.279677 
##            52            53            54            55            56 
##  -5105.050884  -7925.765445   6198.895344  -4068.390761  -4853.068343 
##            57            58            59            60            61 
##   3932.836363    922.185941     -9.977084    161.466547  -4981.193243 
##            62            63            64            65            66 
##  18181.941928   3535.363027  -3769.207340   5848.299504   7226.369962 
##            67            68            69            70            71 
##  14473.778875   1425.059304 -13461.537206  -1412.092178   4561.739339 
##            72            73            74            75            76 
##  -5011.242091  -4460.304214 -10509.148255   2545.363554  -5352.171594 
##            77            78            79            80            81 
##   1150.915936  -6799.322827    663.401542  -2257.639122  -2589.417837 
##            82            83            84            85            86 
##  -3817.979741   -404.859169   2434.861082   3847.817139    518.999125 
##            87            88            89            90            91 
##   -452.211188    228.579994   4327.812946  -1177.288815   1147.870375 
##            92            93            94            95            96 
##  -2077.205003  -1038.261129    191.362163    284.960662  -7477.833849 
##            97            98            99           100           101 
##   2460.453940  -8563.057048  -2833.175145  -3920.712137  -1598.677029 
##           102           103           104           105           106 
##  -1126.006367   3309.826299  -2256.736831   2688.216698  -1098.462413 
##           107           108           109           110           111 
##   1032.384080   2632.628732  -3136.939687  -4681.293436   -773.877132 
##           112           113           114           115           116 
##   1977.229270  11741.464290  -1300.522638   2628.150436   4204.562933 
##           117           118           119           120           121 
##   3415.227569  -1206.404349  -4800.261054  -3757.574075   2321.946297 
##           122           123           124           125           126 
##  -1750.729606   1339.107549   8845.436367    760.317513     47.148123 
##           127           128           129           130           131 
##  -2595.428873   2611.437302   6991.552828    898.927368  -8607.479163 
##           132           133           134           135           136 
##   1726.738584   4100.710279  -3229.833867  -1450.696537   -869.257302 
##           137           138           139           140           141 
##  -3886.387731   1210.185592   -482.112437  -2898.006471   1756.279574 
##           142           143           144           145           146 
##  -1862.693408  -7797.544652   2133.500692  -3415.211192   2187.846678 
##           147           148           149           150           151 
##   -200.918334   1074.260307   -323.617036   1386.056062   1204.341617 
##           152           153           154           155           156 
##   3361.584041  -4886.337844  -1154.903663  -3209.058681   6007.287947 
##           157           158           159           160           161 
##   9739.796890  -3131.806807  -4461.765421   3946.512733    488.439554 
##           162           163           164           165           166 
##   2976.065597  -5664.299015  -6456.865049   4493.325574  17675.382173 
##           167           168           169           170           171 
##   3742.402049   -307.284104  -2339.230064   -965.601100   3744.880759 
##           172           173           174           175           176 
##   -101.872964  -7939.636732   3077.493450   4510.031573    771.397812 
##           177           178           179           180           181 
##   8895.416454  -9177.011996  -3303.586012 -10543.599494 -10945.468900 
##           182           183           184           185           186 
##   1615.816390   9638.179149  -1189.064859   6174.647054   6738.772417 
##           187           188           189           190           191 
##  13279.904050   8436.492816  -4117.062848   2472.743493  10370.946579 
##           192           193           194           195           196 
##  -1719.367192  -2477.712467 -10268.611750  -6238.728567   1421.271254 
##           197           198           199           200           201 
##  -5060.127115  -9575.342081   5688.748235  -2829.396588  -1453.494871 
##           202           203           204           205           206 
##   -540.269530   6753.889178  10067.637028    665.516324   3014.043762 
##           207           208           209           210           211 
##   3167.416932   5834.921057  12842.399001  -5781.801674 -11305.724120 
##           212           213           214           215           216 
##  -5548.087696 -10409.697189  -4798.565482   1838.423118 -12730.519845 
##           217           218           219           220           221 
##  16779.100182   8016.128885   1654.558907  26803.781108  12390.406009 
##           222           223           224           225           226 
##   7113.592763  13780.332992  -4245.643436  -1976.203108   3606.811913 
##           227           228           229           230           231 
##    194.202480   2617.872954   8887.334639   5664.667265  -2083.103897 
##           232           233           234           235           236 
##  -1942.781002   9364.001482 -11630.745466  -7261.035929  -8432.196017 
##           237           238           239           240           241 
##  -9904.780627   3366.096562   1596.561212  -8074.842240  -8695.191126 
##           242           243           244           245           246 
##   9459.009407  -7511.981830   2803.936545 -10026.830283  -3694.785565 
##           247           248           249           250           251 
##   1796.577388   1337.317349 -12012.522660   4047.129999   2401.271422 
##           252           253           254           255           256 
##   4508.413751   2373.416645   -952.932919  11351.276647  20976.773741 
##           257           258           259           260           261 
##   3092.921433  -4377.709794   4074.791363  -1747.999048   3724.422775 
##           262           263           264           265           266 
##  -4881.890454 -10854.736380  -4569.700373   -317.624493  -4985.840766 
##           267           268           269           270           271 
##   9024.642500  -4133.801847   4377.236072  -1966.957031   4591.300412 
##           272           273           274           275           276 
##    822.128495   7411.437622  -1374.299284  12087.975719  -4636.130456 
##           277           278           279           280           281 
##   1740.250578   -361.738772   7879.698527  -5097.455661  -2698.681118 
##           282           283           284           285           286 
## -11187.897362  -2471.023747  18874.407566   7800.289723   2683.612141 
##           287           288           289           290           291 
##   -686.081384    877.434735   6379.195183   6814.364437 -18888.689397 
##           292           293           294           295           296 
## -11025.627750  -7884.406933   9979.591920   3265.989211  -1022.543892 
##           297           298           299           300           301 
##  27570.512022   9938.058216   4697.477323   9303.141429   2582.408915 
##           302           303           304           305           306 
##  -1287.144610   7700.880711 -24534.624177  -3462.367514    -52.657934 
##           307           308           309           310           311 
##  -6837.800221  -3760.274753   3183.054340  -8982.057070  -2922.602777 
##           312           313           314           315           316 
##  -7857.482699   1967.678519  -2791.877485   2420.971761  -3756.119679 
##           317           318           319           320           321 
##  27798.047029   -717.603459   3321.404725  10837.285368   5491.976627 
##           322           323           324           325           326 
##  32250.467425   4656.633423 -21372.793375   1662.779412    996.533583 
##           327           328           329           330           331 
##  -6557.490885  -1722.940991 -33217.507077   1356.204447  -1865.129583 
##           332           333           334           335           336 
##    346.657409  -2751.073698   4516.405484    -77.058401  -6603.718148 
##           337           338           339           340           341 
##  -2702.992734  -1764.545172  -7251.051355   4344.148266   -955.488729 
##           342           343           344           345           346 
##  -1329.681982   -588.147868    571.703262    853.600541  -1270.928922 
##           347           348           349           350           351 
##  -9096.672607 -12768.072369   2876.877939  -3825.195594  -3144.076877 
##           352           353           354           355           356 
##  -5458.593462   2304.991561   1876.934689   3194.738498  -3386.749457 
##           357           358           359           360           361 
##   -114.883302   1060.197409   7366.020126    526.932924    201.683531 
##           362           363           364           365           366 
##   2817.710563  -2551.004876   -645.056851  -8503.463167  -4283.928536 
##           367           368           369           370           371 
##  -5829.028659  -4510.525031  -6779.797328   5544.792694    801.817697 
##           372           373           374           375           376 
##   7518.933659  -7346.666497  -1890.711668  -3006.764909  -2064.259245 
##           377           378           379           380           381 
## -12046.635877   2440.985092 -10157.378394   6269.773485   9796.862524 
##           382           383           384           385           386 
##   3441.731012  -2136.046182   1887.899881   6996.807595  11572.837280 
##           387           388           389           390           391 
##  -5779.202756  -5254.975557     21.735543   8743.612543   1889.137995 
##           392           393           394           395           396 
##  11284.080500  -9943.000952   2854.362602    768.181549    621.629832 
##           397           398           399           400           401 
##   -589.194659   -478.624324 -14386.048465   8816.809523   -999.309750 
##           402           403           404           405           406 
##  -1173.125141   7199.024120  -7803.676502  -1059.852967  -2279.661005 
##           407           408           409           410           411 
##  -5538.107199  -2510.511266  -3545.906691  -8351.189740   6626.544407 
##           412           413           414           415           416 
##   2024.674704  -7030.059263  -7271.715968  14714.093795   4094.469740 
##           417           418           419           420           421 
##   4709.194109  -7880.699086  -4483.190196  -2285.267387   3157.774720 
##           422           423           424           425           426 
## -13722.303925  -2335.034016  -8633.946907   3564.106511   7451.373149 
##           427           428           429           430           431 
##   6930.072729  -3734.199639  -3822.851049  -4382.075318  -1404.348880 
##           432           433           434           435           436 
##  -5323.556230  -6186.676007  -5452.670139   -854.617116   -329.411146 
##           437           438           439           440           441 
##  -4481.670310   3104.489014   5293.399629  -4692.378142  -1748.652065 
##           442           443           444           445           446 
##   1990.022103  -3466.632172   3235.697611  -6235.217738 -11700.467159 
##           447           448           449           450           451 
##  -3973.417688  10202.094791  -1633.623445   5157.469650  -5546.277405 
##           452           453           454           455           456 
##   -738.229778    763.737532   3383.500376 -11963.966545   3815.057818 
##           457           458           459           460           461 
##  -6322.444185   6965.348949   3349.858809   2794.407050  -3596.518891 
##           462           463           464           465           466 
##   2386.558969    252.938169   2049.376959   -290.383192   3587.070324 
##           467           468           469           470           471 
##  -2444.881245   6036.294203  -6781.718570  -2708.825050  -1914.282329 
##           472           473           474           475           476 
##  -4350.631334   3358.987411   8109.060229  -5809.384851   1769.601459 
##           477 
##  -5916.488644 
## 
## $fitted.values
##        2        3        4        5        6        7        8        9 
## 17202.34 20078.03 24374.62 24089.76 26466.70 23772.64 24496.13 19680.49 
##       10       11       12       13       14       15       16       17 
## 19414.41 16730.55 17515.81 14212.44 14264.12 14935.43 16649.00 14952.20 
##       18       19       20       21       22       23       24       25 
## 15997.23 15364.49 22517.87 21592.70 21067.35 22976.29 22295.53 22954.61 
##       26       27       28       29       30       31       32       33 
## 24819.06 18686.63 20430.07 28345.89 28404.13 28073.77 25679.43 27095.92 
##       34       35       36       37       38       39       40       41 
## 30977.44 31329.36 32752.65 30238.32 34183.88 37424.09 34450.71 31229.66 
##       42       43       44       45       46       47       48       49 
## 30065.89 20551.07 28145.10 30605.99 31706.10 38612.69 38102.21 42811.09 
##       50       51       52       53       54       55       56       57 
## 47089.78 39714.57 34228.62 29201.48 22277.25 28630.25 25176.64 21437.16 
##       58       59       60       61       62       63       64       65 
## 25889.67 27161.83 27461.82 27877.76 23707.34 40464.78 42327.21 37525.56 
##       66       67       68       69       70       71       72       73 
## 41774.63 46739.51 57514.51 55508.39 40603.81 38084.69 41132.81 35375.88 
##       74       75       76       77       78       79       80       81 
## 30782.58 21392.92 24626.46 20511.37 22618.32 17462.74 19498.35 18717.13 
##       82       83       84       85       86       87       88       89 
## 17735.12 15784.72 17075.28 20719.47 25181.43 26181.21 26206.42 26829.33 
##       90       91       92       93       94       95       96       97 
## 30995.72 29814.56 30823.92 28868.98 28060.78 28432.61 28843.26 22356.40 
##       98       99      100      101      102      103      104      105 
## 25401.63 18362.32 17207.00 15228.11 15530.86 16215.03 20732.45 19806.78 
##      106      107      108      109      110      111      112      113 
## 23353.03 23140.90 24833.80 27739.37 25212.44 21620.31 21898.49 24571.25 
##      114      115      116      117      118      119      120      121 
## 35544.52 33719.28 35575.15 38603.49 40578.98 38244.26 33013.43 29318.20 
##      122      123      124      125      126      127      128      129 
## 31421.87 29684.61 30877.99 38553.83 38192.71 37244.86 34076.99 35876.02 
##      130      131      132      133      134      135      136      137 
## 41327.93 40762.62 31876.26 33153.72 36375.41 32750.13 31121.26 30197.10 
##      138      139      140      141      142      143      144      145 
## 26719.67 28148.26 27915.58 25578.72 27623.41 26234.40 19772.50 22833.35 
##      146      147      148      149      150      151      152      153 
## 20638.30 23645.20 24190.60 25796.90 25980.80 27651.52 28965.27 32027.77 
##      154      155      156      157      158      159      160      161 
## 27452.62 26708.20 24239.00 30192.06 41152.24 39465.77 36804.34 41874.85 
##      162      163      164      165      166      167      168      169 
## 43297.51 46747.58 42168.15 37428.39 42907.90 59373.17 61607.43 60005.66 
##      170      171      172      173      174      175      176      177 
## 56799.60 55182.83 57912.44 56926.78 49141.79 51993.54 55773.60 55810.15 
##      178      179      180      181      182      183      184      185 
## 63010.30 53417.59 50136.03 40852.75 32307.47 35850.82 46055.35 45505.92 
##      186      187      188      189      190      191      192      193 
## 51518.23 57320.67 68211.51 73547.21 67178.83 67374.20 74515.22 70148.43 
##      194      195      196      197      198      199      200      201 
## 65626.47 54762.73 48733.16 50171.70 45722.34 37812.82 44301.83 42511.49 
##      202      203      204      205      206      207      208      209 
## 42145.84 42628.97 49490.93 58469.06 58094.96 59837.01 61509.36 65338.46 
##      210      211      212      213      214      215      216      217 
## 74899.66 66903.30 54974.23 49529.13 40435.42 37362.72 40507.52 30427.90 
##      218      219      220      221      222      223      224      225 
## 47571.16 54965.16 55876.08 78869.17 86439.12 88462.38 96129.64 86990.06 
##      226      227      228      229      230      231      232      233 
## 80928.47 80506.23 77122.70 76275.81 81060.19 82438.10 76817.92 71983.00 
##      234      235      236      237      238      239      240      241 
## 77693.17 64207.46 56164.34 48034.49 39562.19 43796.01 45970.27 39355.48 
##      242      243      244      245      246      247      248      249 
## 32971.85 43357.12 37546.49 41521.54 33708.07 32400.99 36092.83 38944.95 
##      250      251      252      253      254      255      256      257 
## 29682.73 35680.16 39519.59 44766.30 47511.79 46999.29 57403.23 75075.36 
##      258      259      260      261      262      263      264      265 
## 74888.57 68132.35 69629.00 65812.01 67272.60 60967.88 50135.27 46122.91 
##      266      267      268      269      270      271      272      273 
## 46334.41 42402.21 51294.37 47530.19 51718.39 49816.13 53924.16 54223.13 
##      274      275      276      277      278      279      280      281 
## 60300.73 57911.31 67680.99 61545.04 61757.17 60089.73 65890.03 59557.82 
##      282      283      284      285      286      287      288      289 
## 56087.33 45535.17 43915.88 61320.42 66905.82 67319.37 64711.14 63789.38 
##      290      291      292      293      294      295      296      297 
## 67830.35 71779.69 52586.20 42589.26 36540.41 46965.01 50239.26 49344.35 
##      298      299      300      301      302      303      304      305 
## 73782.66 79787.52 80461.86 85120.45 83301.00 78281.55 81783.05 56430.80 
##      306      307      308      309      310      311      312      313 
## 52654.52 52331.09 46059.13 43240.66 46880.06 39357.75 38067.05 32574.18 
##      314      315      316      317      318      319      320      321 
## 36396.59 35569.74 39439.55 37403.81 63448.17 61267.74 62907.57 70985.74 
##      322      323      324      325      326      327      328      329 
## 73396.96 99133.65 97495.08 73083.36 71869.18 70210.06 62081.23 59174.65 
##      330      331      332      333      334      335      336      337 
## 28822.22 32546.70 32990.63 35333.79 34668.02 40492.77 41579.15 36779.14 
##      338      339      340      341      342      343      344      345 
## 35985.69 36113.62 31385.71 37444.77 38114.82 38375.86 39260.44 41064.26 
##      346      347      348      349      350      351      352      353 
## 42904.50 42653.67 35527.64 26000.98 31399.20 30248.79 29834.74 27427.29 
##      354      355      356      357      358      359      360      361 
## 32153.07 35944.98 40453.32 38624.17 39897.09 42056.98 49526.35 50082.46 
##      362      363      364      365      366      367      368      369 
## 50286.15 52774.00 50232.20 49671.18 42242.64 39411.31 35549.95 33306.37 
##      370      371      372      373      374      375      376      377 
## 29324.64 36685.61 38995.49 46960.10 40871.28 40312.91 38835.54 38363.64 
##      378      379      380      381      382      383      384      385 
## 29139.73 33783.95 26765.94 35067.71 45504.41 49105.62 47361.67 49373.34 
##      386      387      388      389      390      391      392      393 
## 55655.88 65236.49 58379.69 52792.41 52518.39 59972.00 60500.63 69256.29 
##      394      395      396      397      398      399      400      401 
## 58252.64 59835.25 59390.94 58869.62 57341.34 56090.48 42716.19 51388.02 
##      402      403      404      405      406      407      408      409 
## 50378.41 49334.26 55799.82 48267.42 47571.66 45881.54 41515.37 40334.34 
##      410      411      412      413      414      415      416      417 
## 38378.76 32413.60 40365.47 43321.20 37940.00 32978.91 47999.96 51883.38 
##      418      419      420      421      422      423      424      425 
## 55852.13 48245.62 44531.98 43194.65 46817.16 35119.89 34846.38 29047.46 
##      426      427      428      429      430      431      432      433 
## 34693.48 43104.78 50066.20 46799.14 43838.36 40732.63 40619.70 37062.10 
##      434      435      436      437      438      439      440      441 
## 33161.67 30367.90 31959.84 33827.81 31812.37 36727.46 42995.38 39715.08 
##      442      443      444      445      446      447      448      449 
## 39418.12 42454.78 40319.59 44349.22 39548.32 30490.42 29316.19 40787.34 
##      450      451      452      453      454      455      456      457 
## 40465.67 46173.71 41765.94 42119.12 43755.93 47511.54 37283.94 42182.02 
##      458      459      460      461      462      463      464      465 
## 37559.22 45204.43 48759.88 51406.80 48103.44 50467.78 50671.34 52435.95 
##      466      467      468      469      470      471      472      473 
## 51928.50 54901.88 52203.28 57305.29 50497.40 48084.28 46656.20 43246.58 
##      474      475      476      477 
## 47040.51 54578.96 48949.83 50670.20 
## 
## $shapiro.test
## [1] 0
## 
## $levenes.test
## [1] 0
## 
## $autcorr
## [1] "No autocorrelation evidence"
## 
## $post_sums
## [1] "Post-Est Warning"
## 
## $adjr_sq
## [1] 0.8572
## 
## $fstat.bootstrap
## 
## ORDINARY NONPARAMETRIC BOOTSTRAP
## 
## 
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~ 
##     ., parallel = parr)
## 
## 
## Bootstrap Statistics :
##        original     bias    std. error
## t1*    4.737916  0.5821703    2.997483
## t2* 1648.101007 29.6143243  245.336621
## WARNING: All values of t3* are NA
## 
## $itsa.plot
## 
## $booted.ints
##       Parameter   Lower CI Median F-value  Upper CI
## 1 interrupt_var    1.29647       4.850437   10.8872
## 2    lag_depvar 1307.05441    1658.965599 2113.1083

Ahora con las tendencias descompuestas

require(zoo)
require(scales)
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>% 
   dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"electrodomésticos/mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
                                            gasto=="Chromecast"~"electrodomésticos/mantención casa",
                                            gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"electrodomésticos/mantención casa",
                                            gasto=="Sopapo"~"electrodomésticos/mantención casa",
                                            gasto=="filtro agua"~"electrodomésticos/mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Transporte",
                                            gasto=="Uber Reñaca"~"Transporte",
                                            gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
                                            gasto=="Aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
                                            gasto=="Pila estufa"~"electrodomésticos/mantención casa",
                                            gasto=="Reloj"~"electrodomésticos/mantención casa",
                                            gasto=="Arreglo"~"electrodomésticos/mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
    dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
    #dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>% 
#    dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de  diosi. Junio 24, 2019   
    dplyr::summarise(monto=sum(monto)) %>% 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
  ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(size=1) +
  facet_grid(gasto~.)+
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +

  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
  ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
  guides(color = F)+
  sjPlot::theme_sjplot2() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start = 
lubridate::decimal_date(as.Date("2019-03-03"))))

 # scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start = 
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")
library(bsts)
library(CausalImpact)
ts_week_covid<-  
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(fecha_week)%>%
    dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
    dplyr::ungroup() %>% 
    dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
    data.frame()


ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
##  [1]  98.357   4.780  56.784  50.506  64.483  67.248  49.299  35.786  58.503
## [10]  64.083  20.148  73.476 127.004  81.551  69.599 134.446  58.936  26.145
## [19] 129.927 104.989 130.860  81.893  95.697  64.579 303.471 151.106  49.275
## [28]  76.293  33.940  83.071 119.512  20.942  58.055  71.728  44.090  33.740
## [37]  59.264  77.410  60.831  63.376  48.754 235.284  29.604 115.143  72.419
## [46]   5.980  80.063 149.178  69.918 107.601  72.724  63.203  99.681 130.309
## [55] 195.898 112.066
# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na, 
               state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
               family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
               niter = 20000, 
               #burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
               seed= 2125)
## =-=-=-=-= Iteration 0 Mon Aug 01 00:53:11 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Aug 01 00:53:18 2022
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## =-=-=-=-= Iteration 4000 Mon Aug 01 00:53:25 2022
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## =-=-=-=-= Iteration 6000 Mon Aug 01 00:53:32 2022
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## =-=-=-=-= Iteration 8000 Mon Aug 01 00:53:39 2022
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## =-=-=-=-= Iteration 10000 Mon Aug 01 00:53:46 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Aug 01 00:53:53 2022
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##  =-=-=-=-=
#,
#               dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")

impact2d1 <- CausalImpact(bsts.model = model1d1,
                       post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
  ylab("Monto Semanal (En miles)")

burn1d1 <- SuggestBurn(0.1, model1d1)
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d  <- tm_map(corpus, tolower)
d  <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq, 
          max.words=100, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")

fit_month_gasto <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
  dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Otros",
                                            gasto=="Uber Reñaca"~"Otros",
                                            gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
  dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>% 
  dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>% 
    dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
    dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
  data.frame() %>% na.omit()

fit_month_gasto_23<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2023",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_22<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2022",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_21<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2021|2022",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()


fit_month_gasto_20<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("202",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame() %>% ungroup()

fit_month_gasto_23 %>% 
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>% 
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>% 
  janitor::adorn_totals() %>% 
  #dplyr::select(-3) %>% 
  knitr::kable(format="html", caption="Tabla. Gastos promedio por ítem a contar del...",
               col.names= c("Item","2023","2022","2021","2020")) %>% 
  kableExtra::kable_classic(bootstrap_options = c("striped", "hover"),font_size = 12) %>%
  kableExtra::scroll_box(width = "100%", height = "375px")
Tabla. Gastos promedio por ítem a contar del…
Item 2023 2022 2021 2020
Agua NA 6.281714 6.008526 7.529516
Comida NA 300.780286 311.590579 343.078226
Comunicaciones NA 0.000000 0.000000 0.000000
Electricidad NA 43.067143 34.512947 29.129064
Enceres NA 14.113571 14.547842 24.017839
Farmacia NA 3.140000 9.996474 11.560452
Gas/Bencina NA 61.750000 31.329158 25.882387
Diosi NA 19.003857 40.277947 39.056032
donaciones/regalos NA 0.000000 9.056947 8.861903
Electrodomésticos/ Mantención casa NA 6.761143 38.235158 26.757032
VTR NA 27.990000 22.367000 21.107677
Netflix NA 7.505286 7.204316 7.608032
Otros NA 5.401857 1.990158 1.219774
Total 0 495.794857 527.117053 545.807935
## Joining, by = "word"


2. UF Proyectada

Saqué la UF proyectada

#options(max.print=5000)

uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")

tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
    error = function(c) {
      uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
      
    }
  )

tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
    error = function(c) {
      uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
    }
)

uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)

uf_serie_corrected<-
uf_serie %>% 
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>% 
  dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>% 
  dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>% 
   na.omit()#%>%  dplyr::filter(is.na(date3))
## Warning: 35 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)

warning(paste0("number of observations:",nrow(uf_serie_corrected),",  min uf: ",min(uf_serie_corrected$value),",  min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:1682, min uf: 26799.01, min date: 2018-01-01
# 
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>% 
#   dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))

ts_uf_proy<-
ts(data = uf_serie_corrected$value, 
   start = as.numeric(as.Date("2018-01-01")), 
   end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats <- forecast::tbats(ts_uf_proy)


fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)

La proyección de la UF a 298 días más 2022-08-09 00:04:58 sería de: 34.530 pesos// Percentil 95% más alto proyectado: 37.582,14

Ahora con un modelo ARIMA automático


arima_optimal_uf = forecast::auto.arima(ts_uf_proy)

  autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq(from = as.Date("2018-01-01"), 
                                  to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)), 
      tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")), 
                             to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
      tickmode = "array",
    tickangle = 90
    ))
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)

dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="html", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
               col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)")) %>% 
  kableExtra::kable_classic(bootstrap_options = c("striped", "hover"),font_size = 12) %>%
  kableExtra::scroll_box(width = "100%", height = "375px")
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales
Item UF Proyectada (TBATS) UF Proyectada (ARIMA)
Lo.95 33599.83 33568.79
Lo.80 33689.29 33661.54
Point.Forecast 34529.69 36388.94
Hi.80 36218.05 41084.90
Hi.95 37144.98 43570.79


3. Gastos proyectados

Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.

Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
                               col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
                                             "link"),skip=1) %>% 
              dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
              dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
              dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
              dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
              data.frame()

uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>%  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
              data.frame() %>% 
  dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found

ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)], 
   start = 1, 
   end = nrow(uf_serie_corrected_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)], 
   start = 1, 
   end = nrow(Gastos_casa_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)

seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")

autplo2t<-
  autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t

Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.

paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m 
## ARIMA(1,0,0) with non-zero mean 
## 
## Coefficients:
##          ar1      mean
##       0.3280  985.7562
## s.e.  0.1535   38.4750
## 
## sigma^2 = 29191:  log likelihood = -267.98
## AIC=541.96   AICc=542.61   BIC=547.1
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m 
## Regression with ARIMA(1,0,0) errors 
## 
## Coefficients:
##          ar1     xreg
##       0.3618  33.3818
## s.e.  0.1548   1.3979
## 
## sigma^2 = 30136:  log likelihood = -268.65
## AIC=543.3   AICc=543.94   BIC=548.44
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>% 
  dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>% 
  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
            data.frame()
autplo2t2<-
  autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))

dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="html", caption="Tabla. Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
               col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) %>% 
  kableExtra::kable_classic(bootstrap_options = c("striped", "hover"),font_size = 12) %>%
  kableExtra::scroll_box(width = "100%", height = "375px")
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Tabla. Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales
Item Modelo ARIMA con regresor (UF) Modelo ARIMA sin regresor Modelo TBATS
Lo.95 875.1395 631.2725 664.1008
Lo.80 1001.4686 753.9717 743.6058
Point.Forecast 1240.1098 985.7558 920.6852
Hi.80 1478.7510 1217.5400 1219.2415
Hi.95 1605.0801 1340.2391 1414.6897


Session Info

Sys.getenv("R_LIBS_USER")
## [1] "D:\\a\\_temp\\Library"
sessionInfo()
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252  LC_CTYPE=Spanish_Chile.1252   
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C                  
## [5] LC_TIME=Spanish_Chile.1252    
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] CausalImpact_1.2.7  bsts_0.9.8          BoomSpikeSlab_1.2.5
##  [4] Boom_0.9.10         MASS_7.3-54         scales_1.2.0       
##  [7] ggiraph_0.8.2       tidytext_0.3.3      DT_0.23            
## [10] autoplotly_0.1.4    rvest_1.0.2         plotly_4.10.0      
## [13] xts_0.12.1          forecast_8.17.0     wordcloud_2.6      
## [16] RColorBrewer_1.1-3  SnowballC_0.7.0     tm_0.7-8           
## [19] NLP_0.2-1           tsibble_1.1.1       forcats_0.5.1      
## [22] dplyr_1.0.9         purrr_0.3.4         tidyr_1.2.0        
## [25] tibble_3.1.8        ggplot2_3.3.6       tidyverse_1.3.2    
## [28] sjPlot_2.8.10       lattice_0.20-45     gridExtra_2.3      
## [31] plotrix_3.8-2       sparklyr_1.7.7      httr_1.4.3         
## [34] readxl_1.4.0        zoo_1.8-10          stringr_1.4.0      
## [37] stringi_1.7.8       DataExplorer_0.8.2  data.table_1.14.2  
## [40] reshape2_1.4.4      fUnitRoots_3042.79  fBasics_3042.89.2  
## [43] timeSeries_4021.104 timeDate_4021.104   plyr_1.8.7         
## [46] readr_2.1.2        
## 
## loaded via a namespace (and not attached):
##   [1] utf8_1.2.2          tidyselect_1.1.2    lme4_1.1-30        
##   [4] htmlwidgets_1.5.4   munsell_0.5.0       codetools_0.2-18   
##   [7] effectsize_0.7.0    its.analysis_1.6.0  withr_2.5.0        
##  [10] colorspace_2.0-3    ggfortify_0.4.14    highr_0.9          
##  [13] knitr_1.39          uuid_1.1-0          rstudioapi_0.13    
##  [16] TTR_0.24.3          labeling_0.4.2      emmeans_1.7.5      
##  [19] slam_0.1-50         bit64_4.0.5         farver_2.1.1       
##  [22] datawizard_0.4.1    rprojroot_2.0.3     vctrs_0.4.1        
##  [25] generics_0.1.3      xfun_0.31           R6_2.5.1           
##  [28] bitops_1.0-7        cachem_1.0.6        assertthat_0.2.1   
##  [31] networkD3_0.4       vroom_1.5.7         nnet_7.3-16        
##  [34] googlesheets4_1.0.0 gtable_0.3.0        spatial_7.3-14     
##  [37] rlang_1.0.4         forge_0.2.0         systemfonts_1.0.4  
##  [40] splines_4.1.2       lazyeval_0.2.2      gargle_1.2.0       
##  [43] selectr_0.4-2       broom_1.0.0         yaml_2.3.5         
##  [46] abind_1.4-5         modelr_0.1.8        crosstalk_1.2.0    
##  [49] backports_1.4.1     quantmod_0.4.20     tokenizers_0.2.1   
##  [52] tools_4.1.2         ellipsis_0.3.2      gplots_3.1.3       
##  [55] kableExtra_1.3.4    jquerylib_0.1.4     Rcpp_1.0.9         
##  [58] base64enc_0.1-3     fracdiff_1.5-1      haven_2.5.0        
##  [61] fs_1.5.2            magrittr_2.0.3      lmtest_0.9-40      
##  [64] reprex_2.0.1        googledrive_2.0.0   mvtnorm_1.1-3      
##  [67] sjmisc_2.8.9        hms_1.1.1           evaluate_0.15      
##  [70] xtable_1.8-4        sjstats_0.18.1      ggeffects_1.1.2    
##  [73] compiler_4.1.2      KernSmooth_2.23-20  crayon_1.5.1       
##  [76] minqa_1.2.4         htmltools_0.5.3     tzdb_0.3.0         
##  [79] lubridate_1.8.0     DBI_1.1.3           sjlabelled_1.2.0   
##  [82] dbplyr_2.2.1        boot_1.3-28         Matrix_1.3-4       
##  [85] car_3.1-0           cli_3.3.0           quadprog_1.5-8     
##  [88] parallel_4.1.2      insight_0.18.0      igraph_1.3.4       
##  [91] pkgconfig_2.0.3     xml2_1.3.3          svglite_2.1.0      
##  [94] bslib_0.4.0         webshot_0.5.3       estimability_1.4   
##  [97] anytime_0.3.9       snakecase_0.11.0    janeaustenr_0.1.5  
## [100] digest_0.6.29       parameters_0.18.1   janitor_2.1.0      
## [103] rmarkdown_2.14      cellranger_1.1.0    curl_4.3.2         
## [106] gtools_3.9.3        urca_1.3-0          nloptr_2.0.3       
## [109] lifecycle_1.0.1     nlme_3.1-153        jsonlite_1.8.0     
## [112] tseries_0.10-51     carData_3.0-5       viridisLite_0.4.0  
## [115] fansi_1.0.3         pillar_1.8.0        fastmap_1.1.0      
## [118] glue_1.6.2          bayestestR_0.12.1   bit_4.0.4          
## [121] sass_0.4.2          performance_0.9.1   r2d3_0.2.6         
## [124] caTools_1.18.2
#save.image("__analisis.RData")

sesion_info <- devtools::session_info()
dplyr::select(
  tibble::as_tibble(sesion_info$packages),
  c(package, loadedversion, source)
) %>% 
  DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
              caption = htmltools::tags$caption(
        style = 'caption-side: top; text-align: left;',
        '', htmltools::em('Paquetes estadísticos utilizados')),
      options=list(
initComplete = htmlwidgets::JS(
      "function(settings, json) {",
      "$(this.api().tables().body()).css({'font-size': '80%'});",
      "}")))